Review of Outlier Detection and Identifying Using Robust Regression Model

Getnet Bogale Begashaw; Yordanos Berihun Yohannes

doi:doi:10.11648/j.ijssam.20200501.12

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Review of Outlier Detection and Identifying Using Robust Regression Model

Getnet Bogale Begashaw, Yordanos Berihun Yohannes

Published in International Journal of Systems Science and Applied Mathematics (Volume 5, Issue 1)

Received: 25 October 2019 Accepted: 23 November 2019 Published: 13 April 2020

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Abstract

Outliers are observations that have extreme value relations. Herewith leverage is a measure of how an independent variable deviates from its mean. An observation with an extreme value on a predictor variable is a point with high leverage. The presence of outliers can lead to inflated error rates and substantial distortions of parameter and statistic estimates when using either parametric or nonparametric tests. Casual observation of the literature suggests that researchers rarely report checking for outliers of any sort and taking remedial measures for outliers. Outliers can have positive deleterious effects on statistical analyses. For instance, they serve to increase error variance and reduce the power of statistical tests; they can decrease normality, altering the odds of making both Type I and Type II errors for non- randomly distributed; and they can seriously bias or influence estimates that may be of substantive interest. These outliers are cased from incorrect recording data, intentional or motivated mis-reporting, sampling error and Outliers as legitimate cases sampled from the correct population. According to some literatures; Point outliers, Contextual Outliers and Collective Outliers are the three types of outliers. Robust regression estimators can be a powerful tool for detection and identifying outliers in complicated data sets. Robust regression, deals with the problem of outliers in a regression and produce different coefficient estimates than OLS does.

Published in	International Journal of Systems Science and Applied Mathematics (Volume 5, Issue 1)
DOI	10.11648/j.ijssam.20200501.12
Page(s)	4-11
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Break Down Point, Leverage Points, M-estimation, Outlier, Robust Regression Model

References

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[3]	Catherine Stuart, “Robust Regression”, 16^th April, 2011.
[4]	Ekezie Dan Dan And Ogu Agatha Ijeoma, “Statistical Nalysis/ Methods Of Detecting Outliers In A Univariate Data In A Regression Analysis Model”, Imo State University, PMB 2000, Owerri Nigeria.
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[7]	Huber, P. J. (1981), Robust Statistics. John Wiley & Sons, New York.
[8]	Johan COLLIEZ, Franck DUFRENOIS and Denis HAMAD, “Robust Regression and Outlier Detection with SVR: Application to Optic Flow Estimation”, Laboratoire d’Analyse des Systemes du Littoral 50 rue Ferdinand Buisson, BP 699.
[9]	Lianng Yuh and ViII A. Sullivan, “Robust Estimation Using Sas·Softvare”, Department of Biostatistics Merrell Dow Research Institute Cincinnati, Ohio 45215 Mathsoft, Inc. Seattle, WA, 255-298.
[10]	Ranjit Kumar Paul, “Some Methods Of Detection Of Outliers In Linear Regression Model”, Iasri, Library Avenue, New Delhi-110012.
[11]	Robert A. Yaffee, “Robust Regression Analysis: Some Popular Statistical Package Options”, Statistics, Social Science, and Mapping Group Academic Computing Services Information Technology Services December 2002.
[12]	Rousseeuw, P. J. and Leroy, A. M. (1987), Robust Regression and Outlier Detection, Wiley Interscience, New York (Series in Applied Probability and Statistics), 329 pages. ISBN 0-471-85233-3.
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[15]	Yohai V. J. (1987), “High Breakdown Point and High Efficiency Robust Estimates for Regression,” Annals of Statistics, 15, 642-656.
[16]	Yohai V. J., Stahel, W. A. and Zamar, R. H. (1991), “A Procedure for Robust Estimation and Inference in Linear Regression,” in Stahel, W. (A. and Weisberg, S. W., Eds., Directions in Robust.
[17]	Yuliana et al. 2014. M ESTIMATION, S ESTIMATION, AND MM ESTIMATION IN ROBUST REGRESSION. International Journal of Pure and Applied Mathematics, Volume 91 No. 3, 349-360. doi: http://dx.doi.org/10.12732/ijpam.v91i3.7

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Getnet Bogale Begashaw, Yordanos Berihun Yohannes. (2020). Review of Outlier Detection and Identifying Using Robust Regression Model. International Journal of Systems Science and Applied Mathematics, 5(1), 4-11. https://doi.org/10.11648/j.ijssam.20200501.12

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Getnet Bogale Begashaw; Yordanos Berihun Yohannes. Review of Outlier Detection and Identifying Using Robust Regression Model. Int. J. Syst. Sci. Appl. Math. 2020, 5(1), 4-11. doi: 10.11648/j.ijssam.20200501.12

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Getnet Bogale Begashaw, Yordanos Berihun Yohannes. Review of Outlier Detection and Identifying Using Robust Regression Model. Int J Syst Sci Appl Math. 2020;5(1):4-11. doi: 10.11648/j.ijssam.20200501.12

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@article{10.11648/j.ijssam.20200501.12,
  author = {Getnet Bogale Begashaw and Yordanos Berihun Yohannes},
  title = {Review of Outlier Detection and Identifying Using Robust Regression Model},
  journal = {International Journal of Systems Science and Applied Mathematics},
  volume = {5},
  number = {1},
  pages = {4-11},
  doi = {10.11648/j.ijssam.20200501.12},
  url = {https://doi.org/10.11648/j.ijssam.20200501.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijssam.20200501.12},
  abstract = {Outliers are observations that have extreme value relations. Herewith leverage is a measure of how an independent variable deviates from its mean. An observation with an extreme value on a predictor variable is a point with high leverage. The presence of outliers can lead to inflated error rates and substantial distortions of parameter and statistic estimates when using either parametric or nonparametric tests. Casual observation of the literature suggests that researchers rarely report checking for outliers of any sort and taking remedial measures for outliers. Outliers can have positive deleterious effects on statistical analyses. For instance, they serve to increase error variance and reduce the power of statistical tests; they can decrease normality, altering the odds of making both Type I and Type II errors for non- randomly distributed; and they can seriously bias or influence estimates that may be of substantive interest. These outliers are cased from incorrect recording data, intentional or motivated mis-reporting, sampling error and Outliers as legitimate cases sampled from the correct population. According to some literatures; Point outliers, Contextual Outliers and Collective Outliers are the three types of outliers. Robust regression estimators can be a powerful tool for detection and identifying outliers in complicated data sets. Robust regression, deals with the problem of outliers in a regression and produce different coefficient estimates than OLS does.},
 year = {2020}
}

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TY - JOUR
T1 - Review of Outlier Detection and Identifying Using Robust Regression Model
AU - Getnet Bogale Begashaw
AU - Yordanos Berihun Yohannes
Y1 - 2020/04/13
PY - 2020
N1 - https://doi.org/10.11648/j.ijssam.20200501.12
DO - 10.11648/j.ijssam.20200501.12
T2 - International Journal of Systems Science and Applied Mathematics
JF - International Journal of Systems Science and Applied Mathematics
JO - International Journal of Systems Science and Applied Mathematics
SP - 4
EP - 11
PB - Science Publishing Group
SN - 2575-5803
UR - https://doi.org/10.11648/j.ijssam.20200501.12
AB - Outliers are observations that have extreme value relations. Herewith leverage is a measure of how an independent variable deviates from its mean. An observation with an extreme value on a predictor variable is a point with high leverage. The presence of outliers can lead to inflated error rates and substantial distortions of parameter and statistic estimates when using either parametric or nonparametric tests. Casual observation of the literature suggests that researchers rarely report checking for outliers of any sort and taking remedial measures for outliers. Outliers can have positive deleterious effects on statistical analyses. For instance, they serve to increase error variance and reduce the power of statistical tests; they can decrease normality, altering the odds of making both Type I and Type II errors for non- randomly distributed; and they can seriously bias or influence estimates that may be of substantive interest. These outliers are cased from incorrect recording data, intentional or motivated mis-reporting, sampling error and Outliers as legitimate cases sampled from the correct population. According to some literatures; Point outliers, Contextual Outliers and Collective Outliers are the three types of outliers. Robust regression estimators can be a powerful tool for detection and identifying outliers in complicated data sets. Robust regression, deals with the problem of outliers in a regression and produce different coefficient estimates than OLS does.
VL - 5
IS - 1
ER -

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Author Information

Getnet Bogale Begashaw

Department of Statistics, College of Natural Science, Wollo University, Dessie, Ethiopia
Yordanos Berihun Yohannes

Department of Statistics, College of Natural and Computational Science, Salale University, Fitche, Ethiopia

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Getnet Bogale Begashaw, Yordanos Berihun Yohannes. (2020). Review of Outlier Detection and Identifying Using Robust Regression Model. International Journal of Systems Science and Applied Mathematics, 5(1), 4-11. https://doi.org/10.11648/j.ijssam.20200501.12

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ACS Style

Getnet Bogale Begashaw; Yordanos Berihun Yohannes. Review of Outlier Detection and Identifying Using Robust Regression Model. Int. J. Syst. Sci. Appl. Math. 2020, 5(1), 4-11. doi: 10.11648/j.ijssam.20200501.12

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AMA Style

Getnet Bogale Begashaw, Yordanos Berihun Yohannes. Review of Outlier Detection and Identifying Using Robust Regression Model. Int J Syst Sci Appl Math. 2020;5(1):4-11. doi: 10.11648/j.ijssam.20200501.12

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@article{10.11648/j.ijssam.20200501.12,
  author = {Getnet Bogale Begashaw and Yordanos Berihun Yohannes},
  title = {Review of Outlier Detection and Identifying Using Robust Regression Model},
  journal = {International Journal of Systems Science and Applied Mathematics},
  volume = {5},
  number = {1},
  pages = {4-11},
  doi = {10.11648/j.ijssam.20200501.12},
  url = {https://doi.org/10.11648/j.ijssam.20200501.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijssam.20200501.12},
  abstract = {Outliers are observations that have extreme value relations. Herewith leverage is a measure of how an independent variable deviates from its mean. An observation with an extreme value on a predictor variable is a point with high leverage. The presence of outliers can lead to inflated error rates and substantial distortions of parameter and statistic estimates when using either parametric or nonparametric tests. Casual observation of the literature suggests that researchers rarely report checking for outliers of any sort and taking remedial measures for outliers. Outliers can have positive deleterious effects on statistical analyses. For instance, they serve to increase error variance and reduce the power of statistical tests; they can decrease normality, altering the odds of making both Type I and Type II errors for non- randomly distributed; and they can seriously bias or influence estimates that may be of substantive interest. These outliers are cased from incorrect recording data, intentional or motivated mis-reporting, sampling error and Outliers as legitimate cases sampled from the correct population. According to some literatures; Point outliers, Contextual Outliers and Collective Outliers are the three types of outliers. Robust regression estimators can be a powerful tool for detection and identifying outliers in complicated data sets. Robust regression, deals with the problem of outliers in a regression and produce different coefficient estimates than OLS does.},
 year = {2020}
}

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